Many automation applications employ motion control systems to control machine position and speed. Such motion control systems typically include one or more motors or other motion devices operating under the guidance of a controller, which sends position or speed control instructions to the motor in accordance with a user-defined control algorithm. Some motion control systems operate in a closed-loop configuration, whereby the controller instructs the motor to move to a target position or to transition to a target velocity (a desired state) and receives feedback information indicating an actual state of the motor. The controller monitors the feedback information to determine whether the motor has reached the target position or velocity, and adjusts the control signal to correct errors between the actual state and the desired state. Similar control techniques are also used in process control applications. In such applications, the control signal generated by the controller regulates one or more process variables in accordance with a control algorithm, and a measured value of the process variable is provided to the controller as feedback, allowing the controller to adjust the control signal as needed based on the actual value of the process variable relative to a desired setpoint.
Designers of motion control and process control systems seek to achieve an optimal trade-off between performance and system stability. For example, an aggressively tuned controller may result in a system that tracks a desired position with high accuracy and a fast response time, but may be rendered unstable in the presence of system noise and uncertainties. Alternatively, tuning the controller more conservatively will improve system stability, but at the expense of performance. Ideally, the controller gain coefficients should be selected to optimize this trade-off between performance and system stability. The process of selecting suitable gain coefficients for the controller is known as tuning.
Turning the gain coefficients for a controller determines the controller's bandwidth, which is a measure of responsiveness of the controlled system to changes in the control signal. The response of the controlled system to a signal from a controller is partially a function of the controller's bandwidth and the physical characteristics of the controlled system (e.g., inertia, damping, friction, coupling stiffness, phase lag, etc.). In general, higher controller bandwidths will result in faster output response to control signals, better disturbance rejection, and smaller tracking error. However, setting the bandwidth too high can introduce system instability by rendering the system more sensitive to noise and reducing closed-loop robustness (the ability of the system to remain stable over a range of reasonable system uncertainties and disturbances), particularly in the presence of inherently uncertain motor-load dynamics. For example, for lightly damped motion systems, excessively high controller bandwidth can over-excite the system resulting in undesirable oscillations, which in turn may cause controller saturation as the controller attempts to stabilize the resulting oscillations. The system can be rendered more stable by reducing the controller bandwidth, but at the expense of performance. For at least these reasons, tuning parameters for a given motion control system must be carefully selected to achieve robust performance and robust stability.
Adding to these difficulties in achieving optimal controller tuning, some mechanical systems or processes are designed such that there is a considerable delay—known as dead time—between issuance of a control command by the controller and a corresponding system response. Systems with high dead time or phase lag often experience undesirable oscillations in response to disturbances or setpoint changes, resulting in degraded performance and rendering the tuning of such systems more difficult.
The foregoing is merely intended to provide an overview of some of the challenges facing conventional motion and process control systems. Other challenges with conventional systems and contrasting benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.